Computerized search engines can be divided into two main categories: (1) keyword search engines that perform the search by matching keywords to text comprised within texts of textual objects within a corpus of textual objects, and (2) semantic search engines that perform the search in accordance with a semantic meaning of a search phrase provided by a user, trying to “understand” what the user is searching for.
In many cases, current computerized search engines provide a large set of results, which in many cases is unfocused, and miss out on the results that the user desires to obtain. This is true for both keyword search engines and for semantic search engines. In semantic search engines, sentences, or parts thereof, are mapped to corresponding patterns in an attempt to “understand” their meaning. In existing semantic search engines, on top of existing problems of multiple semantic meanings to various sentences, or parts of sentences, the number of patterns to which sentences, or parts thereof, are mapped is very large and cannot be pre-determined. Therefore, there is a need in the art for a new method and system for semantic indexing and searching.
References considered to be relevant as background to the presently disclosed subject matter are listed below. Acknowledgement of the references herein is not to be inferred as meaning that these are in any way relevant to the patentability of the presently disclosed subject matter.
US Patent Application No. 2016/0085853 (Zelevinsky et al.) published on Mar. 24, 2016 discloses a system for performing semantic search receives an electronic text corpus and separates the text corpus into a plurality of sentences. The system parses and converts each sentence into a sentence tree. The system receives a search query and matches the search query with one or more of the sentence trees.
US Patent Application No. 2016/0147878 (Mana) published on May 26, 2016 discloses a Semantic Search Engine using Lexical Functions and Meaning-Text Criteria, that outputs a response (R) as the result of a semantic matching process consisting in comparing a natural language query (Q) with a plurality of contents (C), formed of phrases or expressions obtained from a contents' database (6), and selecting the response (R) as being the contents corresponding to the comparison having a best semantic matching degree. It involves the transformation of the contents (C) and the query in individual words or groups of tokenized words (W1, W2), which are transformed in its turn into semantic representations (LSC1, LSC2) thereof, by applying the rules of Meaning Text Theory and through Lexical Functions, the said semantic representations (LSC1, LSC2) consisting each of a couple formed of a lemma (L) plus a semantic category (SC).
U.S. Pat. No. 4,868,733 (Fujisawa et al.) published on Sep. 19, 1989 discloses a document filing system for storing a large amount of information in proper arrangement for facilitating utilization thereof by a user, while allowing semantical retrieval to be realized even from vague fragmental information. Further, a method is provided for expressing the facts constituting information in terms of “concepts” representing things and “relations” defined between the concepts internally of computer, and a method of inputting user's information to a computer through dialogical procedure and retrieving desired information. Information stored of the computer architects internally a concept network which is displayed in various forms such as hierarchical form based on subsumption relations between the concepts, hierarchical representation based on part-whole relation between the concept, a frame display of a single concepts, and tabular representation of a set of concepts belonging to a given class. The network may be browsed by referring to the contents of the display so that a user can easily know what kind of information has been stored internally of the computer, whereby he or she can perform inputting of new information and retrieval of desired information in a facilitated and simplified manner. The relations stored internally of the computer are classified into “generic relationship” and “instance relation” representing individual facts, whereby a generic framework of facts can be stored. The generic framework is displayed upon interaction with the user for allowing new information to be inputted and desired information to be retrieved in a facilitated and simplified manner. Retrieval by using sematic retrieval formula created internally through dialogical procedure is realized through inferring processing.